Per citar aquest document: http://ddd.uab.cat/record/119263
Approximate ensemble methods for physical activity recognition applications
Casale, Pierluigi

Data: 2014
Resum: The main interest of this thesis is on computational methodologies able to reduce the degree of complexity of learning algorithms and its application to physical activity recognition. Random Projections are used to reduce the learning complexity in Multiple Classier Systems. A new boosting algorithm and a new one-class classication methodology are proposed. In both cases, random projections are used for reducing the dimensionality of the problem and for generating diversity, exploiting in this way the benefits that ensemble learning provides in terms of performances and stability. The practical focus of the thesis is on physical activity recongition using wearable sensors. A new hardware platform for wearable computing application has been developed and used for gathering activity data. Based on the classication methodologies developed and the study conducted on physical activity classication, a machine learning architecture capable to provide a continuous authentication mechanism for mobile-devices users has been designed. The system, based on a personalized classifier, states on the analysis of the characteristic gait patterns typical of each individual ensuring an unobtrusive and continuous authentication mechanism.
Nota: Advisors: Oriol Pujol, Petia Radeva. Date and location of PhD thesis defense: 3 November 2011, Universitat de Barcelona
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: other ; abstract ; publishedVersion
Matèria: Pattern Recognition ; Biometrics ; Biometric Technologies ; Gait analysis
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 22-23, ISSN 1577-5097

Adreça alternativa: http://www.raco.cat/index.php/ELCVIA/article/view/281623


2 p, 58.4 KB

335 p, 17.2 MB

El registre apareix a les col·leccions:
Articles > Articles publicats > ELCVIA : Electronic Letters on Computer Vision and Image Analysis

 Registre creat el 2014-07-29, darrera modificació el 2016-06-04



   Favorit i Compartir